Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q30/00—Commerce
  • G06Q30/06—Buying, selling or leasing transactions
  • G06Q30/08—Auctions
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/90—Details of database functions independent of the retrieved data types
  • G06F16/95—Retrieval from the web
  • G06F16/951—Indexing; Web crawling techniques
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
  • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
  • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y10S707/00—Data processing: database and file management or data structures
  • Y10S707/99931—Database or file accessing
  • Y10S707/99933—Query processing, i.e. searching

Definitions

• the present invention relates to a method and apparatus that allows for enhanced database searching, and more particularly, for use as an internet search engine.
• a web crawler is an automated program which explores and records the contents of a web site and its links to other sites, thereby spreading between sites in an attempt to index all the current sites.
• results are in the form of a list, ranked according to criteria specific to the search engine. These criteria may range from the number of occurrences of the key- words anywhere within the searched text, to methods giving a weighting to key- words used in particular positions (as previously mentioned). When multiple key- words have been used, sites are also ranked according to the number of different keywords applicable.
• a fundamental drawback of all these ranking systems is their objectivity - they are determined according to the programmed c ⁇ te ⁇ a of the search engine, and the emphasis placed on particular types of site design, rather than any measure of the actual users' opinions.
• one embodiment of the present invention provides for a method of updating an internet search engine database with the results of a user's selection of specific web page listings from the general web page listing provided to the user as a result of his initial keyword search entry.
• the database can be updated to prioritize those web listings that have been selected the most with respect to a given keyword, and thereby presenting first the most popular web page listings in a subsequent search using the same keyword search entry
• a method of determining content to provide along with listings transmitted from a server computer to user sites is provided.
• a content listing from each one of a plurality of different developer sites.
• Each of the content listings includes content, a developer identifier, and a keyword, and a keyword selection factor.
• a particular keyword from the obtained keywords that is the same for different content listings. For that particular keyword, the keyword selection factor is used in determining when to transmit different content listings to the user sites.
• a method of updating a keyword table with the results of a user's selection of specific keywords which were obtained from a list of related keywords presented to the user By updating the database with selections of many different users associated with that same keyword, approp ⁇ ate keywords can be provided and presented first when that same keyword is subsequently entered.
• Fig. 1 illustrates certain of the overall features of the present invention
• Fig. 2 illustrates va ⁇ ous inputs to the search, and, for each of the different capabilities, illustrates the outputs that will be provided engine according to the present invention
• Figs. 3 A and 3B illustrates an overview of the process by which web pages are selected m making up the search results provided to the end user according to the present invention
• Fig. 4 illustrates the data sets used for different web-page searches according to the present invention.
• Fig. 5 shows the va ⁇ ous data sets previously desc ⁇ bed, and va ⁇ ous inputs and actions that result m a list of suggested web pages being provided according to the present invention
• Fig. 6 illustrates the implementation of a popular search according to the present invention:
• Fig. 7 illustrates the implementation of a hot off the press search according to the present invention:
• Fig. 8 illustrates the implementation of a high- flyers search according to the present invention:
• Fig. 9 illustrates the implementation of a random search according to the present invention
• Fig 10 illustrates the implementation of a previous past favorites search according to the present invention.
• Fig. 11 illustrates the implementation of a collective search according to the present invention.
• Fig 12 illustrates the implementation of a date created search according to the present invention.
• Fig 13 illustrates the implementation of a customized search according to the present invention.
• Fig 14 illustrates the implementation searching based upon a group identity according to the present invention.
• Fig. 15 illustrates a keyword eliminator feature according to the present invention.
• Fig 16 illustrates the process of determining which search results should be used to make up the cumulative surfer trace table according to the present invention.
• Fig 17 illustrates active suggestion of web pages according to the present invention
• Fig 18 illustrates passive suggestion of web pages according to the present invention.
• Fig. 19 provides an overview of suggesting keywords according to the present invention.
• Fig. 20 illustrates the manner of creating data sets for suggested keywords according to the present invention.
• Fig. 21 illustrates a va ⁇ ety of manners in which a list of suggested keywords can be created according to the present invention.
• Fig. 22 illustrates how content is attached to web page listings according to the present invention.
• Fig. 23 illustrates va ⁇ ous content data sets and operations that populate them according to the present invention.
• Fig. 24 illustrates various content data sets and operations that are used to select data from them a according to the present invention.
• Fig. 25 illustrates web page listings and other content data according to the present invention
• Figs. 1A and IB illustrate certain of the overall features of the present invention, which will be desc ⁇ bed in further detail hereinafter. It is initially noted that like- numbered reference numerals m va ⁇ ous Figures and desc ⁇ ptions will be used in the following desc ⁇ ptions to refer to the same or similar structures, actions or process steps.
• the present invention is preferably implemented in a network environment wherein each computer contains, typically, a microprocessor, memory, and modem, and certain of the computers contain displays and the like, as are well known
• a plurality of user sites/computers 100A-100D are shown, as are a plurality of server computers 102A-B, and developer sites/computers 104A-B. It is understood that in a typical internet network, that different server computers 102 can be interconnected together, as is illustrated. Further, while only a few user sites, developer sites and server computers are shown, it is understood that thousands of such computers are interconnected together.
• step 18 in which the type of search to be performed is selected.
• m step 20 search input obtained from one of a va ⁇ ety of sources is input and used along with the algo ⁇ thm selected in step 18 to determine search results.
• the results of this search are then displayed to the user, as shown by steps of displaying a created list of web pages, displaying passively suggested web pages, and displaying actively suggested web pages, identified as steps 22, 24, and 26, respectively, m Figure 1
• This capability, and how it is implemented, will be desc ⁇ bed m more detail hereinafter.
• step 28 in which the type of keyword search algo ⁇ thm to use is selected.
• step 30 follows in which, based upon a keyword entered by a user, the current set of keyword data is operated upon to determine associated keywords. The results of this operation are then displayed to the user in 30. This capability, and how it is implemented, will be described in more detail hereinafter.
• FIG 1 illustrates certain overall features according to the present invention
• many of the advantageous features of the present invention are not, as mentioned previously, observable to the user, but instead transparent to user. They are, however, significant m order to fully explain how the present invention is implemented and are explained hereinafter.
• Fig. 2 is provided to illustrate va ⁇ ous inputs to the search engine according to the present invention, and, for different capabilities, illustrates the outputs that will be provided. More detailed explanations are provided hereinafter.
• Data that is potentially input from search engine user include- • keyword 52 - this is the word or phrase that the user enter to find a list of web pages
• profile types 54 these are the groups of people they associate them selves with e.g. US, male, doctor etc.
• user ID 56 this is a unique identification for each user that chooses to register with the search engine. This can be done via a cookie or logon.
• search type 58 this can be actively chosen by the searcher to determine the type of search results they would like (popular, new, etc)
• IP address 62 this is passively recorded when a searcher uses the system
• Data from web-page developers include:
• keywords 70 - are the keywords that the web page developer would like to associate their web-
• target audience 72 - these are the target audience (profile types 54) that the web page developer particularly want to target.
• Results from other search engines 80 - these are the results for a keyword search from other existing search engines.
• Outputs of the search engine 10 are: lists of web pages 90 - depending on the input data a list of web pages can be produced m web page determination step 82, desc ⁇ bed further hereinafter; content keywords 92 - the search engine suggests other keywords for users to try produced m key word determination step 84, described further hereinafter; and
• Locations a plurality of unique information entities.
• Web-pages Locations in the form of Web-pages URL (Universal Reference Locator) addresses.
• Key- word The word or phrase that is entered in the search engine
• Hit-list The list of web-pages (URL addresses) that is the result of the key-word search. This hit-list ranks the relevance of the web-pages relative to the key-word. This hit-list always has a key-word associated with it.
• Temporary data set Created only for the duration of the search
• Surfer trace This is a measure of how users search. It is a trace of the key words they search for, the URLs subsequently selected and how long they spend there, from which a ranking of web-pages for a users (surfers) can be calculated. It is a measure of which web-pages they found most useful after the key-word search. The combination of all surfer traces is used to create a users' choice hit-list.
• Users' choice hit-list This a semi -permanent ranking of web-pages associated with every key-word and indicates how useful Internet users found each of the web-pages associated with the key-word.
• the users' choice hit-list is incrementally updated by a new surfer trace.
• New web-page list This is a list of new web-pages that is created by ULR submissions from web-page developers. When a web developer updates a web-page, they can submit the web-page address, b ⁇ ef information about the page and a list of key- words that the developer decides are relevant. The web-page is then placed on the top of each of the key- word new web-page lists.
• Content Provider's list This is a list (associated with each key- word) of content providers which must typically that pay to illustrate content with the key-word. The price paid is dependent on the number of other content providers, the amount they spend and the number of times the key word is searched for.
• High-flyers hit-list This a list of web-pages (associated with every key-word) that are increasing m popula ⁇ ty at the highest rate. It is an indication of how rapidly web-pages are nsmg up the users' choice hit-list and it is used as a means to ensure that new emerging web-pages ⁇ se to the top of the users' choice hit-list.
• Personal hit-list This a list of web-pages the individual user has found most useful for each key-word search they have done in the past. It is like an automatic book-marking data set for each individual user.
• Crawler key-word list This is a list of key- word suggestions that the user may find useful. This is found by matching the key-word entered by the user to the database of key-words and phrases that other users have tned. This is the equivalent of the crawler hit-list, though it is a ranking of key-words rather than Web-pages. The method for doing this uses a similar algo ⁇ thm to a spell-checker only it does it for phrases. It also suggest Key-words, based on previous URL selections from sequences of user key-words.
• Surfer key- word list This is a data set comp ⁇ sed a list of key- words that the individual user found useful after the key-word was selected. This is found by tracking which key-words the user decided to use. This is equivalent to the surfer trace.
• key-word suggester This is a data set consisting of a permanent ranking of other key-words that users have found useful, compiled from successive surfer key-word lists and is linked to each key-word (this is equivalent of the users' choice hit-list).
• Figs. 3A and 3B which provide an overview of the search engine capabilities according to the present invention m which web pages are selected m making up the search results provided to the end user.
• the user enters up to 4 sets of data: keyword 52, profile type 54, search type 58 and User ID 56.
• the IP address 62 and date-time 60 are not entered by the user but can be read when a user uses the search engine
• This data is used is used in parallel in steps 114 and 116 to produce list of web pages.
• Step 114 discussed m detail hereinafter, is the process of selecting web pages from novel new search engine data sets produced in accordance with the present invention.
• step 116 This can run, if desired, in parallel with step 116 which obtains a selection of web pages from other existing search engines. Thereafter, selection of web pages from step 114 and 116 are combined and tagged m step 118.
• the process of tagging the list of web pages desc ⁇ bed in more detail below, enables a set of data, shown as surfer trace data m Fig. 3, to be created and sent back to the search engine when the search engine user selects a web-page from the list in step 120.
• the process of selecting a tagged web- page creates the following series of data which is used to update the search engine data sets; keyword 124 , URL 126, user ID 128, IP address 130, date-time 132, b ⁇ ef web page desc ⁇ ption 134.
• the description 134 will typically only be included in the preferred embodiment of the invention when a new site is added to the data set 114 of the search engine 10, and the desc ⁇ ption used will be that desc ⁇ ption that appears on the original list of web pages.
• the date-time data 132 may only indicate that a site was selected, rather than record the pe ⁇ od of time a user was at a particular site, as explained further hereinafter. This process is invisible to the user who, upon selecting the web-page from the list of web pages is taken directly to the corresponding URL, step 122. Details of the implementation of steps 114, 118 and 120 will be described in more detail hereinafter.
• the user may choose to access another of the web-page URL search results.
• the user may spend time reading, downloading, explo ⁇ ng further pages, embedded links and so forth, or if the site appears irrelevant/uninteresting, the user may return directly back to the search results after a short pe ⁇ od.
• the time difference between the two selections is recorded as the difference between two date/time data 132 from subsequent selections from the list of web page searches (in this embodiment, one can only measure the time spent at one web page if another selection is made after visiting that web page - this then provides another surfer trace 132 which allow a time difference to be calculated).
• This surfer trace data on the popularity of web pages is used to rank the subsequent searches, as desc ⁇ bed further hereinafter.
• the present invention utilizes the cumulative processing and reasoning of all the human users' to provide a vastly more effective means of obtaining the required information sources than is presently possible with the type of method described above.
• human brain power is captured by recording which web pages the user goes to after each keyword search.
• collecting the surfer trace data is achieved by sending, in the list of web pages generated by the search to the user, hidden links that will automatically send information back to the search engine (or a subsidiary server) While the user only sees that his intended link is displayed, the hidden link notifies the search engine of the transfer, which process can be executed with a Java applet.
• the Internet user selects a web-page it takes the user to that address but also sends off a the surfer trace data to the search engine 10, which notes what has been selected.
• another Java applet is then executed which creates another surfer trace.
• the difference between the data time data in this surfer trace from two sequential selections captures the time pe ⁇ od that the user has been at the previous web site. This occurs without the user knowing this data is being sent.
• each applet contains all of the information necessary to update the database at the search engme.
• Another embodiment collects the surfer trace data prior to a user navigating to the intended web site. Other ways of obtaining this surfer trace data are possible and are withm the intended scope of the present invention.
• search results page according to the present invention is therefore differently formatted from conventional search engines' results pages.
• the difference is in action rather than content.
• the page looks the same to the user as standard search results from other search engines.
• the HTTP link associated with the "www.weather.com” label is "http://www.weather.com”. This means that if the user selects this link, they will navigate to this page directly
• the tagged result page for the search made suing the keyword "Weather” may read
• Server side code (application code that runs on the web server) uses this parameter to identify the URL and desc ⁇ ption of the user's chosen site. This information is then stored m a database Table along with other surfer trace data. The server side code then executes a redirect operation to the user's required
• the source of search results is independent to this activity.
• the destination page of the user is independent of this activity.
• the process is one of recording a user, keyword and destination into a database. This method of tracking can only record the initial web-page visited after a keyword search. If the user continues to return to the search results list then subsequent web-page visits can be recorded.
• the surfer trace data that is sent back to the data sets 114 of the search engine 10 as a result of the user selecting the web-page can be encrypted to prevent fraudulent users from sending fake data to the search engine.
• Another method of tracking where a user may connect to from an initial URL selection (if they do not return to the search result page) is to run the selected web-pages as part of a 'frame' located at the search engine web-site. This permits a complete record of the web pages visited to be recorded after a keyword is entered. However, this imposes an additional level of complexity to the system with a possible decrease m system response time.
• the surfer trace data that can be collected includes keyword 124 , URL
• surfer trace is thus a measure of the preferred choices of an individual user or web 'surfers' from the initial search results for a particular set of key-words.How the data sets are created that determine the list of web pages
• Fig. 4 illustrates the data sets used for different web-page searches according to the present invention.
• the data sets (tables) that are used to determine the list if web pages include keyword table 164, profile ID table 166, security table 168, cumulative surfer trace table 170, keyword URL link table 172, personal link table 174, and web-page (URL) table 188.
• the structure of the aforementioned data sets are desc ⁇ bed m more detail hereinafter.
• the desc ⁇ ptions that follow show the data arranged in a spreadsheet fashion, with multiple values per cell and many blank cells. Illustration m this manner is convenient for explaining the present invention, but is not an efficient storage and ret ⁇ eval method.
• a relational database model would be used to implement the data storage according to the present invention such that there may be multiple fields or Tables involved to store the data and each field will store only one value.
• keyword data table 164 of Fig. 4 is shown in more detail m Table 1 shown below, and is a list of keywords, including phrases, and the number of times they have been requested. If the list becomes unmanageably large, the key-words that are not used again after a predetermined time pe ⁇ od could be deleted from the list. However is would be desirable to keep the majo ⁇ ty or all keyword phrases that are entered, if possible.
• Table 1 List of information requests and the number of times it is requests
• web-page table 188 of Fig. 4 contains a list of Internet web-pages.
• Each web-page has a URL address, an associated 2-3 line desc ⁇ ption, a unique web page number for each URL(which can also be any character, symbol code or representation) and the cumulative number of times the URL has been visited.
• the URL address will have a unique number (which can also be any character, symbol code or representation) assigned to it rather than sto ⁇ ng the full URL string m the subsequent data-Tables.
• Keyword URL link table 172 of Fig. 4 contains information about the links between information supplies (URL addresses or web pages) and information requests (keywords).
• This data is recorded m further data sets which descnbes the relationship between the key-words and occurrences as defined by the following three parameters.
• Z is the data time in which a web-page developer submitted a web-page to the search engine.
• the popula ⁇ ty of web pages will be different for different groups of people.
• the inclusion of multiple profile type s will produce multiple values of X Y and Z m Table 3, e.g. one may have a Global and New Zealand popula ⁇ ty rating denoted by XI X2 Yl Y2 etc.
• the global popula ⁇ ty (using the general profile type ) for the rugby and Basketball URL addresses are 520 and 4000 respectively and 52 and 20 respectively for the New Zealand profile type .
• the general profile type setting is used (ranked based on XI)
• the Basketball site would be ranked at the top.
• the New Zealand setting is chosen (ranked based on X2) the rugby site would be highest. This would be a reflection of the preferences of the New Zealanders. This is a very simple method of sto ⁇ ng the preference of different groups of people.
• a "male” and a "New Zealander” would using the search engine increment both X3 and XI. This facility would increase the data requirement of the system but it could vastly improve the search results for different users.
• the total populanty of the web-page needs to be stored as a separate number as users may cont ⁇ bute to more than one of the groups of people. The sum of all of the individual populanty's would be greater than the total populanty because user can belong to more than one profile type.
• a default profile type selection of X's
• selection of X's selection of X's
• other profile type s selection of X's
• a user may have a default profile type of a New Zealand male, but if a technical search is required a "global engineers" profile type may be chosen that reflects the cumulative search knowledge of engineers around the world.
• the extent of personalization could be dependent on the frequency of searching. For example, common keywords such as "news” would have a high degree of personalization (a large range of X values) and less common key-word such as "English stamps” would have little or no personalization (only a global X value).
• the degree of personalization could be a function of the frequency that the keyword is used (found from Table 1). Cumulative surfer trace Table (170)
• cumulative surfer trace table 170 of Fig. 4 The contents of cumulative surfer trace table 170 of Fig. 4 are shown in more detail m Table 4 shown below.
• Information about the links between web pages and keywords m Table 3 (also referred to as keyword URL link table 172) is updated by the surfer trace data.
• the cumulative surfer trace is the combined information from all individual surfer traces and it is used to determine how many "hits" (significant visits) each web-page had for each key-word.
• Table 4 Each row is one surfer trace and the combined rows are the cumulative surfer trace
• profile ID table 166 of Fig. 4 The contents of profile ID table 166 of Fig. 4 are shown in more detail in Table 5 shown below.
• This table includes a unique identification, password, contact email and a default profile type which they normally use to perform their searches.
• the users default profile type is stored as the part of the user's personal preferences profile, which would accessed by entenng some form of personal identification to the system
• This information could be supplied when logging on to the data search engine or the search engine could leave a "cookie", as that term is known in the art, on the computer to identify a user, (there would be an optional e-mail address and password (or similar) associated with the logon procedure).
• the IP address itself would not be a sufficient means of identification as it is not necessarily unique to the individual users.
• the other information can include user defined preferences for how the search results are combined and keywords that are of particular interest to the user. This information can be used to actively customize the search results and suggestions of web pages to visit.
• Table 6 is identical m structure as Table 3, and can be used to record a users personal preferences relating to each URL including the number oftimes visited and the key-words.
• Z is not the date that the web-page developer submitted the web-page by it is the date-time that the user visited the web page. This allow the users could refine a search by defining the last time they visited the web page.
• Table 6 Links between information suppliers (web-pages) and information requests (key-words) for an individual user
• the data in Table 6 is only accessed by the individual that created it, and accessible using a user ID that is preferably independent of changes m the user's e-mail or IP address changes and would thus enable their past personal preferences to be retained du ⁇ ng such changes.
• This Table 6 data set could be stored either at the search engine site or on an individual's computer. Stonng on local PC's would require additional software to be installed on the users computer. There are numerous advantages to storing the information at the search engine including the fact that users are likely to go there more often and unlikely to change search engines once they have a substantial book mark list.
• secu ⁇ ty table 168 of Fig. 4 The contents of secu ⁇ ty table 168 of Fig. 4 are shown m more detail in Table 7 shown below. To ensure that users do not submit the same key-word over and over to increase its populanty the following secu ⁇ ty data table is used. Each entry is a single piece of information I e. yes or no. This table can be created for links between keywords and IP addresses or links between keywords and User ID's
• Table 7 Security Table to ensure one computer user does not submit keywords to artificially boost the popularity of a web-page
• This table is populated every time a user enters a keyword 52 to the search engine.
• a submitted keyword is compared to the keyword list m Table 1 (keyword table 164) and added if it is not already present. If it is present, the cumulative number is increased by one. If the user has a profile type then the cumulative number for the keyword for each type of profile will also be incremented (W1,W2 W3 etc).
• This table is populated a number of ways, including:
• web crawlers may also add URL addresses and desc ⁇ ptions (the desc ⁇ ption is either the first few lines of the web-page or m the HTML coded "title"). This is not an essential element of the system but it could be a method to obtain URL's and desc ⁇ ptions. With this search system web crawlers are more likely to be used to venfy the information rather than find new information.
• the cumulative surfer trace table 170 is populated each time a "tagged" web-page is selected by a user. This sends a packet of surfer trace information, such that the surfer trace data is added to the table each time the user selects another web page from a web page list. Populating the keyword URL link Table 172
• the data from the cumulative surfer trace 170 is used to update the popula ⁇ ty of web pages as recorded in Table 3 (X,Y), also referred to as the keyword URL link table 172.
• Table 3 (X,Y), also referred to as the keyword URL link table 172.
• the frequency of updating Table 3 with the data from the cumulative surfer trace (170) to obtain new values of X and Y is a variable that can be changed, from ranges that are shorter than every hour to longer than every month It should be noted that different keywords can be updated at different intervals of time
• An intermediary step in processing the cumulative surfer trace is to form a cumulative surfer hit table. This is subsequently used to modify the values of and X,Y in Table 3
• the predetermined time used to denote a valid 'hit' may be suitably altered.
• Specialist web crawlers may be employed to independently validate such data.
• the selection of a content provider's banner after a keyword search counts as a hit for their web- page (increment the value of X). This will enable their web pages to possibly go up the populanty list associated with the keyword. This acts as a mechanism to enable a web-page developer to pay to be seen with a keyword. They can not pay to go up the popula ⁇ ty list - this will only occur if people visit their site and spend time there and record a valid hit for the popular list.
• the values of a content hit can vary (e.g. if could be 1 or 0.5 or 7) depending on the emphasis one wants to place how much that content affects the popula ⁇ ty ranking.
• This cumulative surfer trace information can be processed in a large number of ways to populate
• Table 8 (below). Grouping the cumulative surfer trace according to the IP addresses or user ID produces the search pattern for an individual users. This is a list of key- words and URLs and times. This allow the time spent at each web-page to be calculated for each user (it is not possible to calculated the time spent at the last web pages of a search session as there is no time record after they go to that web page)
• Table 8 cumulative surfer hit table created from accumulated surfer traces
• the cumulative surfer hit is used to update the value X in Table 3 in the following way
• HF is the history factor which is a number between 0 and 1.
• the history factor does not have to be the same for every key-word and could be varied depending on the rate at which the keyword is used.
• the data collected for Table 8 is used to recalculate the values of X in Table 3 after a predetermined time period.
• the frequency of updating Table 3 will influence the value of the History factor (HF) chosen.
• the reason for multiplying the existing X by a "history factor” is so that the perceived popularity does not last indefinitely.
• the history factor reduces the weighting attached to the past popularity.
• the key-word "sports news" may have an existing popularity with the following ranking (based on the number of hits per web-page, X)
• the cumulative surfer hit Table for a week may be:
• the database is therefore utilizing the human mmd to provide a powerful indication of what people find useful on the Internet.
• the value of Y in Table 3 is the old value of X, and the value of Y will be updated at intervals that are deemed approp ⁇ ate, which interval could be minutes, hours, days, weeks or longer.
• the update interval does not need to be the same for all different keywords, as previously mentioned. This is used to calculate the rate of change of popularity of web pages and can be used as a selection cnte ⁇ a.
• the cumulative surfer trace includes information on users profiles so Table 8 can be calculated with subsc ⁇ pted values of ⁇ for different profile types. These values of ⁇ n ⁇ 2 ⁇ 3 etc would correspond to the profile types for the subsc ⁇ pted values of X. This allows the popularity of different groups of people to be recorded.
• the simplest method of having new pages recorded by the search engines is for web-page developers to submit information, shown as action 176 in Fig. 4, which information includes URL 66, key-words 70, site descnptions 68, target audience 72 and date-time 74, each time they create or update a web-page.
• This information directly updates Tables 2 (URL table 188 of Fig. 4) and 3 (Keyword URL link table 172 of Fig. 4).
• the URL 66 and desc ⁇ ption 68 are entered in Table 2 and the date-time (74) at which the page is submitted (the Z value) is inserted in Table 3 for each of the key- words (70). Users are allowed a set number of keywords 70 with which they can submit their web page.
• Table 3 An example of what Table 3 would look like with just Z values is given below (format dd-mm-yy).
• the new date is automatically inserted. If a date already exists m the Table, then the dates are compared and if the dates are too close, i.e. less than a pre-determined penod, then the old date remains and the new date is ignored. This stops people from constantly resubmitting to get on the top of the new web page list by resubmitting their web pages. If the URL m Table 3 has other keywords with values of Z closer than the pre-determined pe ⁇ od then the submission is also not allowed. This stops web-page developers from resubmitting their web pages with different sets of keywords.
• the data on new web pages does not necessanly have to be entered by web-page developers. It could be automated by having a web document template that automatically submits data to the search engine whenever the information on the web-page has been significantly changed. It would prompt the web-page developer to change any key- words as approp ⁇ ate.
• Another embodiment requires sending specialist crawlers out to find web site addresses and keywords, though this has many of the drawbacks of existing web-crawlers. It could only be effective if web designers deliberately configured their page with the key-words identified. Any web site designer/prop ⁇ etor willing to do this would also presumably be willing to submit any updates to the search engine to benefit from the instantaneous listing on the search results.
• An extension of this pnnciple is to auto-detect if a web address possessed key-word information in the database and then automatically send an invitation to provide the information to enable their web- page to be found easily.
• the ideal number of key-words to be submitted with each web-page is preferably less than 50 and probably preferable withm the range of about 5 and 20. This also advantageously forces web-site designers to find the most approp ⁇ ate keywords to describe their site and also enable them to choose the audience they wish to target.
• the web-page submission process may also include web-page developer identification process that restncts the ability of people to use the system fraudulently. This may include a payment to prevent multiple web-page submissions. Populating the profde ID Table 166
• ID table 166 of Fig. 4 is populated from the direct inputs from users.
• users search the can choose their profile type 54 from a layered drop down menu, which could include, for example:
• the user selects different profile types from the options they are prompted if they wish to save this as their default profile type. This is then recorded m Table 5 (profile ID's tablel66).
• the user may also select personalization options from a specific personalization options page rather than a drop down menu on the search page.
• the cumulative surfer trace is used to identify the search patterns of individual users based of sorting by User ID 126. This information is used to update the personal link table 174 in the same way that the cumulative surfer trace 170 is used to update Table 3 (keyword URL link table 172). This table stores users past preferences as a form of automatic book marking.
• Table 7 Each time a user enters a keyword 52 into the search engine it updates the secu ⁇ ty table 168 (Table 7) by making a link between the keyword 52 and the IP address 62 (or making a link between the keyword 52 and the User ID 56).
• the data in Table 7 is cleared pe ⁇ odically as the purpose is to stop systematic repeat searching from affecting the popularity lists (value of X m Table 3) rather than stopping individuals who occasionally perform the a repeat keyword search from affecting the popula ⁇ ty list. Determining the list of web pages
• Fig 5 shows the va ⁇ ous data sets previously desc ⁇ bed, and various inputs and actions that result m a list of suggested web pages being provided, and will be desc ⁇ bed in more detail hereinafter.
• user data entered into the search engine can include: keyword 52, user ED 56, search type 58, ? address 62 , profile types 54 How this data can be used to determine a list of web pages 250 as well and deciding which of the list of web pages to tag (step 118 of Fig. 3) for the purposes of creating a surfer trace is descnbed hereinafter.
• Random search hit-list that is a random sample of URLs that have any of the numbers X, Y or Z
• Date created search this is hit-list based on the date time Z and the user-specified date of interest (not just the newest)
• the personal links table 174 also allows past preferences to be listed as search results
• Previous favo ⁇ tes search is a ranked hit-list base on the previous populanty for the individual (X from Table 6) This search is based only on the previous searching of the individual user. This allows the users to very quickly find site that they have previously visited.
• Customized search ranked hit-list that can be a user defined combination of any of the above lists.
• Fig. 5 also illustrates the use of keyword table 164 and secu ⁇ ty table 168 in a decision 246 to send out tagged web pages. This decision is based upon the frequency of key word usage, the data m the security table and the presence of a user identification. The details of the decision to send out tagged web pages is described fully in figure 16.
• Fig 6 illustrates the process for determining a list of popular web pages associated with the entry of a keyword 270 in step 272. If this search is selected and a keyword is entered, step 274 follows and produces a list of web pages based on the values of X taken from Table 3 (172, Fig 5) for the keyword 270 entered. These web pages are identified by a unique web-page(URL) number from Table 3. Thereafter, in step 276 the list of web-page numbers found from step 274 is combined with the URL address and web-page description from Table 2 (188 Fig 5). In step 278 the resulting list of web pages is then tagged, depending on the results of step 246 in Fig 5 as described previously, and sent to the user for them to make their selections. Hot off the press search.
• Fig 7 illustrates the process for determining a list of new web pages associated with the keyword entered in step 290. If this search is selected and a keyword is entered, step 294 follows and produces a list of web pages based on the values of Z taken from Table 3 (keyword URL link table 172 of Fig. 5) for the keyword entered in step 290. These web pages are identified by a unique web-page (URL) number from Table 3. Thereafter, in step 296 the list of web-page numbers found from step 294 is combined with the URL address and web-page description from Table 2 (URL table 188 of Fig. 5). In step 298 the resulting list of web pages is then tagged depending on the results of step 246 in Fig 5 as described previously, and sent to the user for them to make their selections.
• step 294 follows and produces a list of web pages based on the values of Z taken from Table 3 (keyword URL link table 172 of Fig. 5) for the keyword entered in step 290. These web pages are identified by a unique web-page (URL
• Fig 8 illustrates a high-flying web pages search associated with the keyword entered in step 320.
• This is a list of web pages that are increasing in popularity fastest. If this search is selected and a keyword is entered, step 324 follows and produces a list of web pages based on the relationship between the values X and Y taken from Table 3 (172, Fig 5) for the keyword 320 entered. These web pages are identified by a unique web-page (URL) number from Table 3. Thereafter, m step 326 the list of web-page numbers found from step 324 is combined with the URL address and web-page desc ⁇ ption from Table 2
• step 328 the resulting list of web pages is then tagged depending on the results of step 246 m Fig 5 and sent to the user for them to make their selection
• the high-flyer list is calculated by compa ⁇ ng the old popular ranking (Y) and the new popular ranking (X) from Table 3. From this the percentage increase in hits is calculated.
• An alternative method would be to rank the rate of change of populanty by the number of places they rose compared to last time. The formula of calculating the rate of change of populanty for this embodiment is given by:
• the reason for multiplying by the maximum value of X is to ensure that small changes at the lower popularity levels do not swamp more significant changes higher up the table. For example, a web site having previously recorded only one selection and then attracting 5 hits the next day would exhibit percentage increase of 500% whilst another web-page may have expe ⁇ enced an increase from 520 hits to 4000 hits (a much more significant increase) though this would otherwise appear as a lower percentage increase.
• step 356 the list of web-page numbers found from step 354 is combined with the URL address and web-page desc ⁇ ption from Table 2 (188 Fig 5).
• step 358 the resulting list of web pages is then tagged, depending on the results of step 246 in Fig 5 as desc ⁇ bed previously, and sent to the user for them to make their selections. Conventional search.
• Fig. 10 illustrates a previous past favo ⁇ tes search, that is based only on the previous searching of the individual user. This allows the users to very quickly find sites that they have previously visited and performs, therefore, automatic book marking. It should be noted that since a password is preferably used to logon to the search engine system according to the present invention, the user will be able to access their personal preferences from any computer.
• step 374 follows dunng which it is determined what are the favo ⁇ te sites (based on previous usage) for that keyword from the personal link table 174 illustrated m Fig. 5. Because the user has a password that can be used to logon to the system the user will thus be able to access their personal preferences form from any computer. Due to this search capability there is, therefore, no need to manually bookmark web pages. If a user forgot to book-mark a good site on, for example, 'marbles', they can easily find it by retyping the keyword that lead them to that site. If a user's preferences change they will be reflected in the personal links table 174.
• Another embodiment of the personal preference search includes specifying the date the web page was last visited, with or without using a keyword.
• the web pages are then ranked based on Z in personal links table 174 of Fig. 5. For example if a user looked at a site in the middle of last year the user can refine the search by date, thus making it easier to find a previously useful web-pages more easily, even if they could not remember the relevant keyword.
• This automatic book-marking feature can also act as a device for momto ⁇ ng the type of Internet use being undertaken by a particular computer and thus for example, can provide warning to parents/employers of children/employees accessing undesirable sites, such as adult web-pages.
• notification of such usage is automatically provided by letter to the parent/employer thathsts the keywords selected and web pages visited by the children/employees. This information is found directly from each user table
• the collective search is the default search according to the present invention and is used when the user does not actively choose on of the other search options.
• step 402 Upon entry of a keyword in step 402, that keyword is used to select from a combination of web page selections associated with that keyword. As shown, for example, in step 404, an equally weighted combination of conventional, popular, highflier, new and past search results is used to obtain a list of web page numbers Thereafter, in step 406 the list of web-page numbers found from step 404 is combined with the URL address and web-page descnption from Table 2 (188 Fig 5).
• step 408 the resulting list of web pages is then tagged, depending on the results of step 246 in Fig 5 as descnbed previously, and sent to the user for them to make their selections, the system is first configured, the search engine 10 database will not posses any information on popular, high flyers and new web page hit- hsts, so search results will initially be obtained from the conventional hit-list (normal search engine), and the tagged web pages then used to create the database sets as have been desc ⁇ bed. As the system develops, the data sets associated with each of the other search types will become populated, and searches using the other search types will become more useful.
• Fig 12 illustrates a date created search that allows the user to select the date that the web-page was submitted This feature will only work for web-pages that contain a date created data entry, identified as date-time submission 74 m Fig. 4.
• the search engine 10 Upon entry of a date-time and/or a keyword in step 432, the search engine 10 will perform step 434 in which a list of web page numbers associated with these vanables is obtained. Thereafter, m step 436 the list of web-page numbers found from step 404 is combined with the URL address and web-page descnption from Table 2 (188 Fig 5). In step 438 the resulting list of web pages is then tagged, depending on the results of step 246 in Fig 5 as described previously, and sent to the user for them to make their selections.
• Fig 13 illustrates a customized search that allows the user to decide how they want their default hit- hst to appear.
• the keyword and User ID is selected in order to initiate the customized search.
• P ⁇ or to initiating the customized search in step 466 which step is identical to step 404 of the collective search previously desc ⁇ bed with respect to Fig. 11 , however, step 464 is applied to customize the users default mixture of hit-lists For example a user may want their default search results to include only popular and new web pages but no high flying web pages.
• This custom search is then performed m step 466 to generate a list of web page numbers.
• step 468 the list of web-page numbers found from step 466 is combined with the URL address and web-page desc ⁇ ption from Table 2 (188 Fig 5).
• step 470 the resulting list of web pages is then tagged, depending on the results of step 246 in Fig 5 as desc ⁇ bed previously, and sent to the user for them to make their selections, one preferred embodiment, the make-up of the default search results list can be amended by 'learning' from the user's behavior to create a changing customized search based on the user's own search patterns. If a user consistently chooses new web pages or high-flying web pages for example, then their set of default search results will be changed to reflect their normal search style.
• the magazine search according to the present invention enables users to search by following a se ⁇ es of menu-d ⁇ ven subject choices (or similar hierarchical structure), rather than ente ⁇ ng a specific key-word(s).
• Fig. 14 when a user enters a keyword and User ID in step 490, the default profile of the user can be used to reflect the type of web pages that people of the same "group" as the user profiles desire to see.
• the search that takes place in step 494 is based on the subsc ⁇ pted X,Y and Z values obtained from the default profile of people of those "group" affiliations identified m the user's personal profile obtained m step 492.
• search results are obtained particulanzed for the group that the user identifies with.
• the resulting list of web pages, de ⁇ ved from steps 496 and 498, as have been previously desc ⁇ bed, are particula ⁇ zed for that group.
• the user can select different profile types for different searches dunng a single session and is not be restncted to the default profile types
• a level of authentication for person's of a certain group to have their search results actually be used for purpose of updating the database relating to that group.
• doctors who have a user ID that identifies them as doctors may perform a search related to a certain medical condition, and their selections can be tagged and used m the database for that group of doctors as has been previously descnbed.
• patient's may desire to identify their profile with that of the same group of doctors, their selections are not as significant as those of the actual doctors, and thus while they are able to view the web page listings that doctors deem most pertinent, their selections are not used to update the doctor's group database, since their IDs do not identify them as a doctor
• keyword eliminator feature which is illustrated m Fig. 15, and prevents certain users, such as children, from searching for undesirable keywords and web-pages when the keyword eliminator feature is turned on.
• the present inventor's have realized that it is potentially much easier for example, to stop children searching for pornography, rather than attempting to trace and prevent access to all sites on the Internet with pornographic content. This would be used as a complimentary tool to existing "net nanny" type devices.
• a preexisting table inaccessible keywords is stored in a table and compared m step 522 with a keyword previously entered, as shown by step 520.
• keywords that are inaccessible will not be searched.
• parents could choose the types of keywords 552 that they do not want their children to search for - and this will be different for different sets of parents.
• the system filters out the keywords that may be used for subsequent searching in step 524. Determining which users to sample
• Fig 16 illustrates the process of determining which search results should be sampled and used to make up the cumulative surfer trace table 170 of fig. 4, also referred to as Table 4. While possible, it is not necessary to collect data concerning every single search, and this can be controlled by determining which sets of results get sent out with "tagged" web pages. Reference with respect to this was already mentioned with respect to authenticating user's of a particular group, doctors in the example provided.
• step 556 for a user that has a user ID and has chosen to use the personal links table
• step 558 when a keyword is submitted, a check is made that the IP address 62 has not already searched the keyword using secunty table 168 (Table 7) before the user is sent a set of tagged results. If so, the user can still undertake the search though it will not cont ⁇ bute to the cumulative surfer trace 170 (Table 4). This allows all normal users to affect the popular hit-list and all users to search whatever they would like, but prevents fraudulent users, such as spammers, from contributing to the popular hit-list.
• the secu ⁇ ty table 168 can also include information on links between keywords 52 and a user ID 56 to detect repeat searching.
• step 560 popular keywords can be traced once every tenth, hundredth, or even thousandth occurrence, and the frequency of this selection can be changed to optimize the system.
• the frequency of keyword usage is determined from keyword table 164 as shown in Fig. 5 (Table 1).
• the frequency of sending out tagged results can also be linked to the rate at which populanty is changing for different key words. For example the keyword "IBM” would probably have IBM's home page at the top and most user's would go there, whereas the key word “latest fads" may have a constantly changes set of web pages that needs to be sampled more frequently.
• Fig 17 Another feature of the present is illustrated by Fig 17, and involves using data to actively suggest web pages. This is different from a search because the user sets up the request and is informed if there is any new data on the subject. To do this the users has to actively specify which keywords they are interested m and the profile type that they would like to act as a filter or agent and the search type (new, highflying, popular) in step 588. This information is stored in the user's profile ID 166 shown m Fig. 5 (Table 5).
• the user receives a list of suggested web pages determined by a group of like minded humans. For example a user may choose to be notified of web pages with the following
• the suggested web-sites can be displayed for the user when they next access the search engine or they may choose to be notified of these suggested web pages via e-mail notification. This way web pages can be drawn to the user's attention without any active searching for these keywords. Passive suggestion of web pages to visit
• FIG 18 Another feature of the present is illustrated by Fig 18, and involves automatic web-page suggestion based on how the user has searched m the past and requires no active input from the user.
• the system can be activated passively, at va ⁇ ous intervals or times (such as at each login to the search engine), by looking at which keywords, profile types and search types, the users frequently looks at using the personal links table 174 of Fig. 5 (Table 6). For example, it may be that the user frequently looks at rugby information as a "New Zealand, male” and looks at decay treatments as a "dentist". This information can be found from the automatic book marking table, previously referred to personal links table 174. If the user has not looked at these sub j ects for a certain length of time and there are new or highflying information sources, the user will be automatically notified of these new information sources.
• a penodic e-mail can be sent out with the two newest and highest flying sites related to the key- words of the user.
• the present invention also provides for a data set 642 that provides synonyms for the keywords entered along with the particular profile type in step 640.
• the system represented in Fig 19 is referred to as a key word suggester. This is implemented, in one embodiment, by matching the key-word entered by the user m step 640 with the existing key-words and phrases m keyword table 164 of Fig. 5 (Table 1) that other users have t ⁇ ed using other search methods, identified in step 646.
• Each keyword is then tagged in step 660, and those that are selected by a user in step 662 are used to form a keyword surfer trace 648 as shown in Fig. 19, which contains the o ⁇ ginal keyword 52 that the user entered, the keyword selected 652, and the IP address 130, user ID 128 and date-time 132 data as m the previously descnbed web page surfer trace.
• the data from the cumulative keyword surfer trace 648 is then used to reinforce links between keywords. In this way the system learns which keywords are associated with each other. The system learns which words are related to each other in the same way that the system learns which URL's are associated with the key-words. The lists of suggested keywords will become more relevant over time as the relevancy is improved each time the keyword suggester is used.
• a keyword link table 696 and a cumulative keyword trace table 698 are used along with the previously desc ⁇ bed secu ⁇ ty table 168 to create the data sets for suggested keywords.
• the key- word link table 696 shown in Table 10 below, records how often each key- word is selected from the suggested key-word list. This can then be used to rank the of the usefulness of different key-words relative to each other.
• Keyword 3 was found useful 1134 times after trying keyword 2.
• keyword 2 was found useful only 356 times after users fried key- word 3.
• Information about the links between keywords m Table 10 is updated by the information about how people are using suggested keywords (keyword surfer traces 648).
• the cumulative keyword surfer trace 698 is the combined information from all individual keyword surfer traces 648 and it is used to determine how many "hits" (significant visits) each keyword had for each key-word.
• the information collected from each individual surfer trace is a series of inputs become a cumulative keyword surfer trace, shown in table form below m Table 11.
• Fig 20 also illustrates how links between keywords m Table 10 can be initiated by recording sequences of keywords that users put into the search engine. If, for example someone searches using the keyword "NHL” and then "National Hockey League", this would then draw an association between these two key- words in Table 10 by recording this as one hit. Again this captures the reasoning power of users to define the link between two keywords. Often the keyword in sequence will be totally unrelated to the previous key- word but sometimes it will be relevant. If the next user chooses it from the key word selector it will reinforce the key-word link m the same way that repeat selection to web pages reinforces links between a keyword and a URL. The following is an example of keywords that may be suggested after ente ⁇ ng the a simple key- word like "Book"
• phrases All of these key- words (phrases) would come from information seekers (users) and information providers (web-page developers). The most appropriate keywords will emerge naturally over time.
• the keyword suggester trace can store the most recent keyword links and modify the main key-word trace by a history factor, m the same way as Table 3 is modified by the cumulative surfer trace.
• the cumulative keyword surfer trace 698 is processed in the same way as the cumulative web-page surfer trace 170 of Fig 5 to reinforce links between keywords in the keyword link table 696 (Table 10).
• a time va ⁇ able can also be included so that if a user chooses another keyword very quickly it is assumed that the previous keyword was not useful and is not counted as a keyword surfer trace.
• the individual keyword suggester can store, for each user, their personal keyword links.
• the keyword suggester can be based on a number of different profile types.
• the word associations may be quite different for people of different culture, nationality, occupation and age etc.
• Keyword hits in Table 10 can be subscnpted m the same way that the values of X,Y and Z are subscnpted for different types of profiles in Table 3, as explained previously.
• Fig. 21 illustrates a vanety of manners in which a list of suggested keywords can be created.
• One manner is by ranking the values of X m the keyword link table 696 (Table 10). This ranked list of keywords is combined with keywords from a normal search of keywords, descnbed previously with respect to step 646 of Fig. 19. nother manner of suggesting keywords, shown as step 730, is to compare the popular list (URLs X values) for the user-entered key-word with the popular-list of other key- words in Table 3.
• a similanty pattern X values m Table 3 indicates that these keywords are similar. For example a user may search for "film reviews" and the keyword suggester may come up with "movie reviews" which has a more comprehensively searched list of sites. In this case there is no physical similanty between the words movie and film, but they are linked by the similanty of the patterns of URLs links they have in common m Table 3.
• step 744 The usefulness of the key word suggester list is enhanced indicated by step 744, by associating with each key-word on the suggestion list an indication of whether there are any of the aforementioned searches available (popular, high flyer, etc.) for that key- word in keyword URL links table 172 of Fig. 5 (Table 3). The keywords with the most search results are then highlighted.
• the secu ⁇ ty table 168 and keyword link table 696 are used to determine which keyword links to sample in a manner similar to that previously desc ⁇ bed with respect to tagging web pages. As with the decision for tagging web pages this can depend on whether it is a repeat keyword (found from secu ⁇ ty table 168) and on the frequency of keyword usage (found from keyword table 64), as well as the considerations previously discussed.
• va ⁇ ous different web pages listings and web pages are displayed as has been descnbed
• One common characte ⁇ stic of each these different web page listings that have been desc ⁇ bed is that when they are displayed they appear substantially identical to one another. As shown in
• listings 902 are many times larger than the listings 900, may include graphical content, and appear more prominent when displayed to the user Such listings can contain the same content as a web page listing, or other content, such as advertisements, pictures, edito ⁇ als and the like.
• This other content may be displayed to a particular user based upon key-words, user profile type (nationality, age , gender, occupation, and so forth) and the time of the day, for example.
• this content that is displayed along with web page listings is inserted into the display area using mechanisms that are different from the searching system desc ⁇ bed previously with respect to conventional search engines.
• the mechanism by which this content is displayed m large measure based upon some other c ⁇ tena, such as payment for the space that is used. While the system for selecting this content works, it is difficult to keep track of which content was displayed when, especially if that content is frequently changed.
• another aspect of the present invention which will now be discussed, is a system for tracking changing content, and allowing for content providers to dynamically select when their content will be displayed. This dynamic selectable content, as illustrated m Fig.
• step 762 may be displayed to the viewer based upon keyword or profile type as entered by the viewer in step 762 as shown
• the content selector step 764 that then follows, the time of the day is considered and used in selecting the appropnate content 902 as illustrated m Fig. 25 along with the web page listings 900.
• Each content 902 transmitted with the search results made up of web page listings 900 is tagged in step 766.
• m step 770 that content 902 is displayed, typically simultaneously with content 900
• this content embodiment also provides for the web page developer, or content provider, to determine the frequency with which this content will be reviewed, and, depending upon the patterns of users with respect to web page listings that are viewed, alter the manner in which the content provider's content 902 is displayed based upon key words, user profile and the like
• Keyword content data table 804 is illustrated in more detail in Table 12 below, and its charactenstics are: H is the cumulative number of hits for one time penod for the keyword. This is the number of times people choose that keyword;
• N is the number oftimes particular content 900 that is associated witha keyword has been sent out for display. This is not necessa ⁇ ly the same as H since content associated with a profile type may be have a different selection factor than content associated with the keyword.
• This selection factor can be vanous va ⁇ ables, such as votes or pnce
• A is the selection factor for the keyword from each content provider (e.g. a selection factor could be a S bid to be associated with that keyword);
• T is the total of the selection factors for each keyword and is the sum of A's
• P is the content value, as determined by votes or p ⁇ ce, for each keyword and is T/N (e.g. this could be the $ per time content is sent out with that key word- this is a p ⁇ ce of being associated with that key word)
• This Table can also include the maximum content value M that the content provider is prepared to give. There is no limit to the number of content providers that may attempt to have content 902 displayed with a web page listing that is associated with a particular keyword.
• provider's of content 902 could target both the key- word and the audience by identifying each of the keywords with target audiences, e.g. the number of hits associated with the word rugby could be broken down into the different profile type s that search for the word rugby.
• the cumulative number of searches for rugby could be 6000 split into 520 under 21 's and 4000 21-50 year olds and 520 50+ age group. Thus, there may be a different content value for each of these sub classes within a keyword search.
• Table 13 determines the content value of the content 902 to specific audiences of people as opposed to different keywords and allows for targeting of specific audiences.
• Content provider data table 812 of Fig. 23 is illustrated in more detail below as Table 14 and contains information about the content provider, such as name, address, advertiser, content information such as the Bitmap (HTML or Java applet or similar) that the content 902 will use and a unique number to identify each different item of content 902.
• Table 14 contains information about the content provider, such as name, address, advertiser, content information such as the Bitmap (HTML or Java applet or similar) that the content 902 will use and a unique number to identify each different item of content 902.
• This Table may also store details of the content provider, such as passwords, payment details (e.g. credit card number and authonzation), content delivery (number of times content has been sent to users) etc.
• details of the content provider such as passwords, payment details (e.g. credit card number and authonzation), content delivery (number of times content has been sent to users) etc.
• N is the number of times content is sent out associated with the keyword. This is incremented each time an item of content 902 is displayed to a user that is specifically associated with that keyword 810.
• the values for A 802 are selected by content providers for each keyword.
• the content provider can also enter a maximum value M over which they will no longer select to be sent out with the keyword.
• T is the total for each keyword and is the sum of As
• P is the content value, as determined by votes or p ⁇ ce, for each keyword and is T/N Populating the personal profile content data
• H is the cumulative number of hits for each profile type and this information is taken directly from Table 1 (sum of the indexed W's).
• N is the number of items of content 902 sent out associated with the personal profile. This is incremented each time an item of content 902 is sent out that is specifically associated with that profile type 810
• the values for A 808 are placed, through an entry process akm to bidding, for each profile type.
• the content provider can also enter a maximum M they are prepared to pay, or vote, as the case may be.
• T is the total for each profile type, and is the sum of As.
• P is the content value for each profile and is T/N Populating the content provider's details table
• the majonty of the content provider's details 812 are electronically entered by the content providers Each time a content provider's content 902 is sent out this event is also recorded m the content provider's details Table 812. This will also record the number of click-throughs (820,822,824,826,828) and the cost, in terms of payment or votes, of the content 902. This will form the basis of the electronic bill or tabulation that is thereafter forwarded to the content provider. How the data sets are used to select content sent out to users
• a keyword and profile type are submitted to the search engine step 852.
• keyword content data table 804 personal profile content data table 806, the value of content 902 for each is found from the value of P m the Tables.
• the details for the content item are taken from Table 14, content provider details table 814 and transmitted to the user in step 868. Details of the content items 902 transmitted for each content provider are also sent to the content provider, as shown by step 870, at regular intervals.
• the system compares the value of the content for all the possibilities (keyword, combinations of profile types) and sends out the content that has the most value, as determined m step 862.
• an under 21 male may search using the key- word "Rugby" and the value for the associated content for rugby is 0.1 per view, whereas the value per view for targeting an under 21 male is 0.2 and thus the content targeted at the male under 21 would be displayed rather than the rugby content.
• N the cumulative frequency of times that content items 902 are transmitted
• H the total cumulative frequency for the targeted area
• the cumulative frequency (H) of the number of times 'rugby' is searched for and 'males under 21 ' would both incremented by one (via Table 1).
• the number of times an item of content 902 is displayed would be incremented only for the 'male under 21 ' Table (this is the figure used to determine the value of the content per unit view.
• Determining which specific content is transmitted The example below shows how content associated with the keyword is selected. It is the same process for content associated with profile types.
• the new content provider then enters the selection factor A and the system can then instantly calculate the new value (P) based on the new total bids (T).
• the advertiser can also be told the number of views per month they are likely to get for their bid (N*(A/T)). These changes are calculated in real-time to give the new content provider an indication of how their bid will influence the value and the views they will receive for their bid. If a value and number of views are agreeable to the advertiser they can choose to submit it as a bid for the defined penod, such as a day, week, or month, for instance.
• the details of other content providers are, preferably, not made public. Content providers may also enter a maximum value M they can part with for their content.
• This provides content providers with some secunty against paying too much if the value changes. If the value goes too high then a content provider's bid can drop off the list (if P is greater than M then A is not counted as a bid for that particular content provider). The bid would go back on the list if the value went down again, thus acting as a stabilizing mechanism.
• the content provider can, m a preferred embodiment, be notified by e-mail if their content 902 has dropped off the list due to their value limit M .
• content providers thus have an account with the search engine prop ⁇ etors and procedures for debiting their account for their content is automatically calculated from the account details on a penodic basis.
• An electronic statement of the number of views, cost per view, number of click-throughs and cost per click-through for each content provider is also forwarded to each content provider, since this information is also stored in content provider details table 812 (Table 14).
• clusters of similar keywords based on the keyword link table.
• the reason for identifying clusters of keywords is so that content 902 can be targeted at groups of words rather than just individual words.
• the cluster for the key-word "car” may include hundreds or thousands of words that have links to the word car (e.g. convertibles, automobiles, vans).
• Statistical cluste ⁇ ng techniques are used to define the size and frequency of key-word clusters. This makes it a much more automatic process than an editor deciding on clusters of keywords for content provider's to target.
• the same system can be used to set values for keyword clusters. While grouping words in this way would incur an increased administration cost, it is nevertheless computationallysimilar and only initiated once a certain level of hits on a keyword had been exceeded .
• Content only search Users can also purposely choose to search only the content provider associated with a keyword.
• the search results will be based on the values of A in Table 12. The content providers that pay the most will be at the top of the list.
• the key- word suggester can also help content providers choose key- words or sets of key-words that they would like to display.
• a high history factor will make it difficult to change the existing popularity lists. As an example if the rate of searching for a particular keyword is increasing quickly, then the history factor should be lower to enable emerging web pages to nse up the popularity list.
• Sampling frequency This is the frequency of sampling the information of how users are searching. If it is a common keyword it is not necessary to monitor every search. It may be that only a percentage of all searches need be monitored to accurately determine web-page popula ⁇ ty.
• the "content hit factor” is a measure of the weighting given to a hit on content being recorded as a hit for a keyword.
• the default setting is that a hit on content counts the same as a hit from the list of web pages.
• the value of content hits can be set higher or lower than unity, depending on the p ⁇ ce of the content, e.g. the "content hit factor” may need to be increased for valuable keywords as this would decrease the ability to spam these commercially valuable keywords.
• the higher the content factor the higher the resistance to spam as the search results would be more dependent on pnce rather than popula ⁇ ty.
• Time pe ⁇ od e.g. one month. This time pe ⁇ od may be different depending on the rate-of-change of the p ⁇ ce. If the price is changing rapidly or is very stable, the time penod may be respectively shortened or lengthened correspondingly.
• This number could be changed to influence how the system learns from new web pages submissions.
• Intranet searches at present suffer from similar drawbacks from Internet searches, indeed some intranets can m themselves be extremely substantial systems, in which identifying a particular information source or item can be equally problematic. Utilizing the present invention in such applications is withm the intended scope of the present invention .
• the present invention is also intended to be applied matching a user's profile to other media sources (such as pay per-view, television, videos, music and the like), thus allowing content targeted to a particular audience.
• Other media sources such as pay per-view, television, videos, music and the like
• search lists as described above (Popular-list, High- flyers, Hot-off the press, etc) may be employed to direct users to appropnate material.
• the search techniques desc ⁇ bed herein can be implemented a consumer network to assist shoppers m selecting items from withm one shop or among a large number of shops
• a keyword-item purchased link Table that then records what items were purchased after each shopping request (key-word).
• This embodiment also records where the user purchased the product. Each time a shopper purchased an item this would increment the populanty of that item, using the same techniques described previously.
• the profile type s m this embodiment can be used to record the types of purchases made by different sets of people.
• the same content bidding mechanism could be used to determine the pnce of content for any location on the Internet, not just web page listings as identified above.
• content providers will bid for a general content space to set the p ⁇ ce automatically.
• the profile type information from the search engine could be used as a passport so that other advertisements on the Internet could be more targeted to different audiences. This profile type information could also be used by web-page developers to customize their web-page for different sets of users.
• the system according to the present invention can be used as a dating service and/or a method for matching people with similar preferences by doing a statistical analysis to compare the individual preferences (Table 6) of groups of users.
• the individual past preference Tables, m this embodiment would preferably be normalized and compared to each other using a standard correlation coefficient. When compared to other users it would give a nume ⁇ cal indication of how similar their preferences are.
• the same embodiment could also be used to find information about similar people from there past preferences Tables. For example one could ask to be give the names of people in New Zealand with an interest m Ecological Economics and a search could be made of the personal preferences Tables. Such an embodiment, however, would typically include a password/consent indicator that provides consent of identified persons to give out their information, which consent could be given, for example, in only certain circumstances, which circumstances are limited to searchers who have a level of authority and password indicating the same, or for persons who identify themselves with certain characte ⁇ stics.

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